[R] Problem with Autocorrelation and GLS Regression

[and_mue]

Hi,

I have a problem with a regression I try to run. I did an estimation of the
market model with daily data. You can see to output below:

/> summary(regression_resn)
Time series regression with "ts" data:
Start = -150, End = -26
Call:
dynlm(formula = ror_resn ~ ror_spi_resn)

Residuals:
       Min         1Q     Median         3Q        Max 
-0.0255690 -0.0030378  0.0002787  0.0039887  0.0257857 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)
(Intercept)  -0.0003084  0.0007220  -0.427    0.670
ror_spi_resn  0.0363940  0.0706150   0.515    0.607

Residual standard error: 0.008016 on 123 degrees of freedom
Multiple R-squared: 0.002155,	Adjusted R-squared: -0.005958 
F-statistic: 0.2656 on 1 and 123 DF,  p-value: 0.6072 /

I did several tests for assessing the quality of the estimation (like
breusch-pagan, breusch-godfrey, chow-breakpoint, arch lm tests).  The model
has now clearly a problem with autocorrelation as you can see in de images
below:
http://r.789695.n4.nabble.com/file/n4631336/resid_resn.png 
http://r.789695.n4.nabble.com/file/n4631336/pacf_resid_resn.png 
To take into account the problem of autocorrelation, I did a gls estimation
with an AR(1) process and get the following output: 

/> summary(gls(ror_resn~ror_spi_resn, correlation=corARMA(p=1),
method="ML"))
Generalized least squares fit by maximum likelihood
  Model: ror_resn ~ ror_spi_resn 
  Data: NULL 
        AIC       BIC   logLik
  -859.0308 -847.7176 433.5154

Correlation Structure: AR(1)
 Formula: ~1 
 Parameter estimate(s):
       Phi 
-0.3182399 

Coefficients:
                   Value  Std.Error    t-value p-value
(Intercept)  -0.00034277 0.00052344 -0.6548430  0.5138
ror_spi_resn  0.04337265 0.06741179  0.6433986  0.5212

 Correlation: 
             (Intr)
ror_spi_resn -0.159

Standardized residuals:
        Min          Q1         Med          Q3         Max 
-3.21202187 -0.38283220  0.03863226  0.50313857  3.24224614 

Residual standard error: 0.007953852 
Degrees of freedom: 125 total; 123 residual/

I plot acf and pacf again to assess the changes in autocorrelation. But
interestingly, there is no change in the plots, they are equal to the images
above...

Can anyone give advice on how to handle this problem?  There is the
possibility that I am clearly on the wrong path. I am still a beginner in
using R. Furthermore, I did the same procedure with EVIEWS (also
implementing AR(1) process) and the model gives different results for the
coefficients and error terms. 

Regards
Andi

/Output EVIEWS:

Dependent Variable: ROR_RESN				
Method: Least Squares				
Date: 05/25/12   Time: 17:17				
Sample (adjusted): 2 125				
Included observations: 124 after adjustments				
Convergence achieved after 7 iterations				
				
Variable	Coefficient	Std. Error	t-Statistic	Prob.  
				
C	-0.000409	0.000525	-0.779074	0.4375
ROR_SPI_RESN	0.052996	0.067794	0.781716	0.4359
AR(1)	-0.314260	0.085592	-3.671586	0.0004
				
R-squared	0.104144	    Mean dependent var		-0.000365
Adjusted R-squared	0.089337	    S.D. dependent var		0.007945
S.E. of regression	0.007581	    Akaike info criterion		-6.902354
Sum squared resid	0.006955	    Schwarz criterion		-6.834122
Log likelihood	430.9460	    Hannan-Quinn criter.		-6.874637
F-statistic	7.033211	    Durbin-Watson stat		2.070520
Prob(F-statistic)	0.001289			
				
Inverted AR Roots	     -.31			
				/

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